Weakly-supervised learning scheme requires coarse-grained or noisy labels during the training phase. Following the work of [1]}, weakly-supervised action detection in common settings requires only the video-level labels of actions during training while the temporal boundaries of action instances are not needed. During testing both labels and temporal boundaries of actions are predicted. In the following parts of this section, weakly-supervised action detection refers to this setting. There are also other weak signals utilized for action detection such as order of actions [2]}, [3]}, [4]}, [5]}, frequency of action labels [6]}, and total number of events in each video [7]}. A common strategy in weakly-supervised action detection is to use attention mechanism to focus on discriminative snippets and combine salient snippet-level features into a video-level feature. The attention scores are used to localize the action regions and eliminate irrelevant background frames. There are two main strategies to extract attention signals from videos. First, class-specific attention approaches where attention scores are generated from class activation sequences (def REF ) for each action class (Section REF ). Second, class-agnostic attention approaches where attention scores are class-agnostic and are extracted from raw data (Section REF ). We discuss these two attention strategies in this section.
